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Environmentally-viable utilization of chicken litter as energy recovery and electrode production: A machine learning approach

Author

Listed:
  • Lee, Seonho
  • Kim, Jiwon
  • Byun, Jaewon
  • Joo, Junghee
  • Lee, Yoonjae
  • Kim, Taehyun
  • Hwangbo, Soonho
  • Han, Jeehoon
  • Kim, Sung-Kon
  • Lee, Jechan

Abstract

Waste-to-energy could eventually be a crucial role in new disposal strategy for organic waste like chicken litter. Herein, a pyrolysis-based process that fully utilize the pyrolysates of chicken litter as energy sources (gas and liquid fuels) and energy storage material (supercapacitor (SC) electrode), which significantly contributes to carbon neutrality, has been proposed. Feasibility and readiness of the proposed process were estimated by combining various approaches such as lab-scale experiment, full-scale process simulation, life cycle analysis, and machine learning. Pyrolytic products other than the SC electrode material, such as gas and liquid, could be considered as fuels given their higher heating values ranging from 6 to 7 MJ kg−1. The SC electrode is comprised of porous carbon and delivers a large specific capacitance of 90 F g−1 at a constant specific current of 0.1 A g−1, demonstrating a potential for use in energy storage. Compared with fossil-based electrode materials, the chicken litter-based electrode material had 49%–64% reduction in global warming potential (GWP) and 83% of abiotic depletion of fossil fuels. The implementation of an SC system using the chicken litter-derived electrode therefore has potential to reduce global warming and fossil resource depletion. In addition, a case study on Jeju Island in the Republic of Korea incorporating renewable energy networks was considered based on diverse samples and their forecasts from machine learning algorithms. The chicken litter-based SC system in Jeju Island could reduce 63,191 t CO2 eq. per year of GWP.

Suggested Citation

  • Lee, Seonho & Kim, Jiwon & Byun, Jaewon & Joo, Junghee & Lee, Yoonjae & Kim, Taehyun & Hwangbo, Soonho & Han, Jeehoon & Kim, Sung-Kon & Lee, Jechan, 2023. "Environmentally-viable utilization of chicken litter as energy recovery and electrode production: A machine learning approach," Applied Energy, Elsevier, vol. 350(C).
  • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923011467
    DOI: 10.1016/j.apenergy.2023.121782
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    1. Kazmi, Hussain & Munné-Collado, Íngrid & Mehmood, Fahad & Syed, Tahir Abbas & Driesen, Johan, 2021. "Towards data-driven energy communities: A review of open-source datasets, models and tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    2. Kim, Jiwon & Park, Chanyeong & Park, Hoyoung & Han, Jeehoon & Lee, Jechan & Kim, Sung-Kon, 2022. "Upcycling of cattle manure for simultaneous energy recovery and supercapacitor electrode production," Energy, Elsevier, vol. 258(C).
    3. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    4. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    5. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    6. Kim, Soosan & Byun, Jaewon & Park, Hoyoung & Lee, Nahyeon & Han, Jeehoon & Lee, Jechan, 2022. "Energy-efficient thermal waste treatment process with no CO2 emission: A case study of waste tea bag," Energy, Elsevier, vol. 241(C).
    7. Izabella Maj, 2022. "Significance and Challenges of Poultry Litter and Cattle Manure as Sustainable Fuels: A Review," Energies, MDPI, vol. 15(23), pages 1-17, November.
    8. Kim, Sehyun & Lee, Hyunjae & Kim, Heejin & Jang, Dong-Hwan & Kim, Hyun-Jin & Hur, Jin & Cho, Yoon-Sung & Hur, Kyeon, 2018. "Improvement in policy and proactive interconnection procedure for renewable energy expansion in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 150-162.
    9. Rahman, Aowabin & Smith, Amanda D., 2018. "Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms," Applied Energy, Elsevier, vol. 228(C), pages 108-121.
    10. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
    11. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    12. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
    13. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    14. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    15. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    Full references (including those not matched with items on IDEAS)

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